Fast identification and removal of sequence contamination from genomic and metagenomic datasets.
Fast identification and removal of sequence contamination from genomic and metagenomic datasets.
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DOI：
10.1371/journal.pone.0017288
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发表时间：
2011-03-09
期刊：
影响因子：
3.7
通讯作者：
Edwards R
中科院分区：
文献类型：
--
作者：
Schmieder R;Edwards R
高通量测序技术对微生物学产生了重大影响，提供了一种快速且经济有效的方法来生成基因组草案和探索微生物多样性。然而，从不纯的核酸制剂获得的序列可能含有来自样品以外来源的DNA。这些序列污染严重影响下游分析所用数据的质量，导致序列重叠群的错误组装和错误的结论。因此，去除测序污染物是所有测序项目的必要步骤。我们开发了 DeconSeq，这是一个强大的框架，用于快速、自动识别和去除较长读长数据集中的序列污染（平均读长为 150 bp）。 DeconSeq 以独立版本和基于网络的版本公开提供。结果可以导出以供后续分析，并且用于网络版本的数据库会定期自动更新。 DeconSeq 对可能的污染序列进行分类，消除与非污染基因组具有更高相似性的冗余命中，并提供比对结果和分类的图形可视化。使用 DeconSeq，我们对先前发表的 202 个微生物和病毒宏基因组中可能存在的人类 DNA 污染进行了分析，发现 145 个 (72%) 宏基因组中可能存在污染，污染序列高达 64%。这个新框架使科学家能够自动检测并有效地从数据集中去除不需要的序列污染，同时消除当前方法的关键限制。 DeconSeq 的网络界面简单且用户友好。独立版本允许离线分析并集成到现有的数据处理管道中。 DeconSeq 的结果揭示了测序实验是否成功、样品是否测序正确以及样品是否含有来自 DNA 制备或宿主的任何序列污染。此外，对 202 个宏基因组的分析表明非人类相关宏基因组存在显着污染，表明该方法适合筛选所有宏基因组。 DeconSeq 可从 http://deconseq.sourceforge.net/ 获取。
High-throughput sequencing technologies have strongly impacted microbiology, providing a rapid and cost-effective way of generating draft genomes and exploring microbial diversity. However, sequences obtained from impure nucleic acid preparations may contain DNA from sources other than the sample. Those sequence contaminations are a serious concern to the quality of the data used for downstream analysis, causing misassembly of sequence contigs and erroneous conclusions. Therefore, the removal of sequence contaminants is a necessary and required step for all sequencing projects. We developed DeconSeq, a robust framework for the rapid, automated identification and removal of sequence contamination in longer-read datasets (150 bp mean read length). DeconSeq is publicly available as standalone and web-based versions. The results can be exported for subsequent analysis, and the databases used for the web-based version are automatically updated on a regular basis. DeconSeq categorizes possible contamination sequences, eliminates redundant hits with higher similarity to non-contaminant genomes, and provides graphical visualizations of the alignment results and classifications. Using DeconSeq, we conducted an analysis of possible human DNA contamination in 202 previously published microbial and viral metagenomes and found possible contamination in 145 (72%) metagenomes with as high as 64% contaminating sequences. This new framework allows scientists to automatically detect and efficiently remove unwanted sequence contamination from their datasets while eliminating critical limitations of current methods. DeconSeq's web interface is simple and user-friendly. The standalone version allows offline analysis and integration into existing data processing pipelines. DeconSeq's results reveal whether the sequencing experiment has succeeded, whether the correct sample was sequenced, and whether the sample contains any sequence contamination from DNA preparation or host. In addition, the analysis of 202 metagenomes demonstrated significant contamination of the non-human associated metagenomes, suggesting that this method is appropriate for screening all metagenomes. DeconSeq is available at http://deconseq.sourceforge.net/.
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影响因子：
12.3
作者：
Huse SM;Huber JA;Morrison HG;Sogin ML;Welch DM
通讯作者：
Welch DM
影响因子：
48
作者：
Mavromatis, Konstantinos;Ivanova, Natalia;Kyrpides, Nikos C.
通讯作者：
Kyrpides, Nikos C.
影响因子：
7
作者：
Li, Heng;Ruan, Jue;Durbin, Richard
通讯作者：
Durbin, Richard
影响因子：
12.3
作者：
Langmead B;Trapnell C;Pop M;Salzberg SL
通讯作者：
Salzberg SL
DOI：
10.1093/bioinformatics/btp698
发表时间：
2010-03-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Li H;Durbin R
通讯作者：
Durbin R